A Deployable Model and Global Dataset on Stormwater Microplastic Concentrations and Drivers

Reshadi, Mir Amir Mohammad

https://ror.org/01aff2v68

University of Waterloo

https://orcid.org/0000-0001-7975-5083

Rezanezhad, Fereidoun

https://ror.org/01aff2v68

University of Waterloo

https://orcid.org/0000-0002-9608-8005

Shahvaran, Ali Reza

https://ror.org/01aff2v68

University of Waterloo

https://orcid.org/0000-0001-7846-054X

Ghajari, Amirhossein

https://ror.org/01aff2v68

University of Waterloo

Kaykhosravi, Sarah

https://ror.org/04mte1k06

National Research Council Canada

https://orcid.org/0000-0002-9481-8991

Stephanie, Slowinski

https://ror.org/01aff2v68

University of Waterloo

https://orcid.org/0000-0001-5090-441X

Van Cappellen, Philippe

https://ror.org/01aff2v68

University of Waterloo

https://orcid.org/0000-0001-5476-0820

2016/2024

2025-02-19T20:17:08Z

2025-02-19T20:17:08Z

2025-02-19

https://doi.org/10.20383/103.01159

https://www.frdr-dfdr.ca/repo/dataset/a144aad5-192f-4c05-b167-7a10604a74e1

This dataset compiles global measurements of stormwater microplastic concentrations along with their driving factors, serving as a resource for analyzing the extent and variability of microplastic pollution in stormwater systems. The dataset integrates measured microplastic concentrations from diverse locations with environmental, hydrological, meteorological, and land use data, offering a structured foundation for further analysis and modeling. To enhance its utility, three machine learning models were developed and interpreted to examine the relationships between stormwater microplastic concentrations and various hydrometeorological and socioeconomic variables. The dataset is intended to support future modeling efforts, particularly when supplemented with additional empirical studies. The provided model can be deployed in stormwater catchments where cost-intensive sampling and analysis are limited or used as a basis for further refinement and development.

Federated Research Data Repository / dépôt fédéré de données de recherche

Creative Commons Attribution 4.0 International (CC BY 4.0)

https://creativecommons.org/licenses/by/4.0/

Microplastics

Urban Stormwater

Concentration Data Base

Machine Learning

CatBoost

A Deployable Model and Global Dataset on Stormwater Microplastic Concentrations and Drivers

f163c1b3-9c88-42f6-a7bb-5839ed6c4063

/1/published/publication_1154/

2025

Mir Amir Mohammad Reshadi

2022/2024

Dataset

https://doi.org/10.1038/s41598-025-90612-0

Gumi;Gumi;North Gyeongsang;South Korea

Cheonan;Cheonan;South Chungcheong;South Korea

Gwanpyeong-dong;Gwanpyeong-dong;Daejeon;South Korea

Gothenburg;Gothenburg;Västra Götaland;Sweden

Sundsvall;Sundsvall;Västernorrland;Sweden

Newark;Newark;New Jersey;United States

San Francisco;San Francisco;California;United States

El Cerrito;El Cerrito;California;United States

New Jersey;New Jersey;New Jersey;United States

Tijuana;Tijuana;Baja California;Mexico

Calgary;Calgary;Alberta;Canada

Saskatoon;Saskatoon;Saskatchewan;Canada

Vaughan;Vaughan;Ontario;Canada

Kitchener;Kitchener;Ontario;Canada

Wuhan;Wuhan;Hubei;China

Shanghai;Shanghai;Shanghai;China

Beijing;Beijing;Beijing;China

Xifeng;Xifeng;Guizhou;China

Paris;Paris;Île-de-France;France

Pathum Thani;Pathum Thani;Pathum Thani;Thailand

Gold Coast;Gold Coast;Queensland;Australia

Perth;Perth;Western Australia;Australia

Bushehr;Bushehr;Bushehr;Iran

Tokyo;Tokyo;Tokyo;Japan

https://ror.org/01h531d29

Natural Sciences and Engineering Research Council

ALLRP 558435 – 20

Plastics science for a cleaner future

RDF1050706

Earth and related environmental sciences

Hydrology

Water quality

Sciences de la Terre et sciences de l'environnement connexes

Hydrologie

La qualité d'eau

RDF1020104

Computer and information sciences

Artificial intelligence (AI)

Machine learning

Informatique et systèmes d'information

Intelligence artificielle (IA)

Apprentissage machine

This dataset contains the modeling code and data used in the study titled "Assessment of Environmental and Socioeconomic Drivers of Urban Stormwater Microplastics Using Machine Learning," published in Scientific Reports. Users are encouraged to supplement the dataset with additional findings to enhance its applicability and relevance.